Unsupervised visual tracking. 
Since it is costly to collect annotations for videos, unsupervised tracking [1]}, [2]}, [3]}, [4]} becomes a promising approach for training more robust trackers.
The pioneering work UDT [5]} trained a Discriminative Correlation Filters (DCF) based tracker by forward-backward tracking frames with the supervision of consistency loss.
These works indicated that cycle consistency in videos can be utilized to effectively train a robust tracker by forward-backward tracking multiple frames.
On the other hand, \(s^2\) siamfc [2]} proposed a Siamese network based unsupervised training framework by mining the self-supervision in single frames, in which adversarial masking is learnt to construct template-search pairs from identity frame.
However, tracking performance of these methods [4]}, [5]} depends heavily on online updating schemes.
Without online updates, these unsupervised trained trackers cannot handle challenging objects with dramatic variation.
Recently, acquiring self-supervision signals from both spatial and temporal dimension becomes the promising way for unsupervised tracking.
Zheng et al.  [4]} proposed an unsupervised training approach by naive training from single frame in first stage, then cycle training is adopted to learn on longer temporal spans.
PUL [3]} proposed to initially learn a background discrimination model by contrastive learning. Then the model continues training with temporal corresponding patches mined with a noise-robust loss.
Besides, various pretext tasks [11]}, [12]}, [13]} are constructed in learning visual representation from videos by utilizing the forward-backward tracking idea.
Different from these works, our framework mainly focuses on learning classification and regression capability simultaneously from the temporal supervision, achieving superior tracking performance.
